Bibliometric analysis of hotspots and frontiers in cancer-related fatigue among ovarian cancer survivors

Objectives To explore and analyze research hotspots and frontiers in CRF in ovarian cancer patients to provide an evidence-based basis for scholars and policymakers. Background Ovarian cancer is one of the most common and lethal gynecological malignancies. Cancer-related fatigue (CRF) is an annoying and pervasive side-effect that seriously affects the activities of daily living and decreases the quality of life (QoL) of cancer survivors. Methods The literature was retrieved from the Web of Science Core Collection (WOSCC) from inception to 2021-12-31. CiteSpace was used to discuss research countries, institutions, authors, and keywords. Results This study ultimately included 755 valid publications, and the number of publications showed a gradual upward trend. The countries, institutions, authors, and journals that have published the most articles and cited the most frequently were the United States, the University of Texas MD Anderson Cancer Center, Michael Friedlander and Amit M Oza, Gynecologic Oncology, and Journal of Clinical Oncology. The top three high-frequency keywords were Ovarian cancer, chemotherapy, and clinical trial. The top three keywords with the strongest citation bursts were cyclophosphamide, double-blind, and open-label. Conclusions Conducting multi-center, large-sample, randomized controlled clinical trials to determine whether chemotherapeutic agents have severe adverse effects and to discuss the relationship between CRF and QoL and overall survival in cancer survivors are hotspots in this field. The new trends may be applying double-blind, randomized controlled trials to clarify the causes of CRF and open-label, randomized trials to determine the efficacy, safety, and tolerability of chemotherapeutic agents.

following reasons. Firstly, the Web of Science is an international multidisciplinary citation index publication on citation statistics and is recognized worldwide as the most authoritative indexing tool for scientific and technical literature, consisting of the WOSCC and other databases [24,25]. Secondly, the Web of Science is also the largest and most comprehensive resource for academic information covering a wide range of disciplines, with data updated to reflect dynamic changes in the scientific field timely [26]. Moreover, the Web of Science has the advantage of being a bibliographic database that provides references cited in scholarly publications [27].

Data processing
The WOSCC was searched for investigating literature on CRF in women with ovarian cancer from inception to December 31st, 2021. The first article on CRF in ovarian cancer patients in the WOSCC was issued in 1991, and the literature search date was December 31, 2021; therefore, the timespan of our study was 1991.01.01-2021.12.31. The retrieval subject terms are ovarian cancer and cancer-related fatigue. A total of 768 original records were retrieved. The retrieved documents were imported into CiteSpace to remove duplicates and then independently screened by 2 researchers to exclude papers that did not fit the research topic and search strategy. After excluding editorial materials (n = 3), meeting abstracts (n = 8), and proceedings papers(n = 2), 693 articles and 62 reviews related to the topic were finally obtained.

Data analysis
CiteSpace is a Java application and bibliometric software developed by Professor Chaomei Chen [28]. CiteSpace applies co-citation analysis theory and pathfinder network algorithms to extract information from scientific literature, converting the information into visualized mapping knowledge maps and presenting the research evolution, hotspots, and frontiers of a given area [29,30].
In this study, CiteSpaceV5.8 R3 visualization software was used for keyword extraction, keyword burst detection, and clustering analysis. Keyword burst detection and keyword clustering analysis were performed using the g-index algorithm. Each node in the map represents a keyword, and a line between two nodes indicates a connection between two nodes [31]. The larger the node, the more frequently the keyword appears. Node centrality represents the connection between a node and other nodes, as well as the position and role of a node in the whole network [30,32]. If the centrality of a node is higher than 0.1, it means that the node has a relatively core position in the network [33]. In the timeline diagram, the red part of the green timeline represents the starting and ending years of the cited keywords, and "strength" represents the strength of the cited keywords [34].

Analysis of annual publications
The volume of scientific literature publications could reflect the dynamic process of research ups and downs and reflect the level of scientific research in a particular field [35]. With 755 sorted and detected papers as samples, the publication volume of literature in the 31 years from 1991 to 2021 was statistically analyzed in the time dimension, mapping out the chronological distribution of relevant studies (Fig 1).
Three  [38]. Since then, research on this theme has begun to sprout.

Performance of countries /regions
The country was set as node type and time slice was set as 1 year, and 755 records of CRF in ovarian cancer patients published from 1991 to 2021 were analyzed to generate a collaborative country network map with 61 nodes, 375 links, and a density of 0.2049 (Fig 2). Each node in the figure represents a country (or a region), and the size of the node is proportional to the papers published by countries [39]. The links between nodes represent cooperation between countries, and the thickness of the links is positively correlated with the number of articles issued.
The top 10 active countries in this research field (n> = 40) are listed in Table 1. Combining  Fig 2 and Table 1 and counting the total number of publications in CiteSpace showed that 1,236 papers were published in 61 countries, with 938 papers published in the ten most active countries, far exceeding the total literature related to the topic. It showed strong links between research countries and frequent cooperation between researchers in this field. The top 10 countries accounted for 75.9% of the total publications, while the United States had the most publications with 426, accounting for 34.5% of the total publications. The United States, Australia, Italy, and France have a centrality of over 0.1.

Performance of institutions
With the institution as the node type and time slice of 1 year, 755 records of CRF in ovarian cancer survivors were analyzed to generate a knowledge map (Fig 3). Pathfinder and pruning sliced networks were used to present a more intuitive and understandable map. 634 institutions studied CRF in ovarian cancer patients. Each node in Fig 3 represents an institution, and the size of the node is proportional to the papers published by institutions. The links between nodes represent cooperation between institutions, and the thickness of the links is positively correlated with the number of articles issued. The top 10 productive institutions are listed in Table 1

Analysis of authors
The author was set as node type, and years per slice was 1 year and generated a collaborative author map with 798 nodes, 1369 links, and a density of 0.0043 (Fig 4). As seen in Fig 4, the nodes represent the researchers in the field, and the node size is proportional to the papers published by researchers. The links between nodes reflect the collaboration between authors, and the thickness of the links is proportional to the outputs. Core authors refer to researchers

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with high academic attainments, extensive publications, and profound influence in a particular field. Price's Law [40] defines core authors by the number of papers published, and its calculation formula is M = 0.749 � Nmax1/2 (Nmax refers to the number of papers published by the most productive author in the statistical years, and those who published more than M papers are core authors of this research area). The most productive author is Michael Friedlander and Amit M Oza, with 17 publications. According to the calculation formula, Nmax and M for this study area were 17 and 6.4, respectively. Therefore, authors with publications of 7 or more were the core authors. The core authors in this domain are listed in Table 2. Combined to Fig  4 and Table 2, 798 authors published 755 papers, while 119 papers were published by the 11 core authors, accounting for 15.8% of the total publications in the field.  Table 3.

Research hotspots and frontiers on CRF in ovarian cancer survivors
Keywords could be accurately extracted throughout the paper, and their frequency is a kind of mapping of the research hotspots. CiteSpace can identify the frontier of a certain research field by detecting burst words with high frequency and fast growth. This section used the keyword as node type to analyze the main research hotspots and frontiers from keyword co-occurrence and clustering and keyword bursts. To determine research hotspots and frontiers in this area, we extracted the top 10 keywords from 755 articles and listed them in Table 4.

Keyword co-occurrence and clustering
688 keywords were extracted from 755 relevant articles, as shown in (Fig 5). Nodes represent keywords in this area, and links represent the relationship between nodes. The larger the node and the thicker the line, the more crucial the node is.

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As shown in Table 4, the subject terms of this study are cancer-related fatigue and ovarian cancer, ranking in the 8th and 1st of the top 10 keywords, respectively. Other the most frequent keywords related to our study are chemotherapy, clinical trial, quality of life, and cancer survivor. The clinical trial, QoL, and cancer survivor have high centrality (cen-trality> = 0.1).
In this study, the LSI algorithm recommended by Professor Chaomei Chen was applied to cluster the keywords, and the keyword clustering knowledge map is generated after adjustment and filtering, as shown in (Fig 6). According to the clarity of the network structure and clustering, CiteSpace provides the modularity (Q) index and average silhouette score to evaluate the drawing effect of the map. The average silhouette score and Q score are both between -1 and 1, and the higher the average silhouette score, the more reliable the clustering is; the higher the Q score, the better the structure of the network [41,42]. The Q score of this study is 0.7768, and the average silhouette score is 0.9029, which can be considered that this clustering is significant and convincing. Fig 6 shows 14 clustering modules with different color blocks to distinguish each cluster. The 14 clustered modules are labeled with #0-#13, the outline of the module is marked with the corresponding number, and the core keywords of the module are displayed within each module. The keyword font size and the circle size in front of them are proportional to the frequency of the keyword. The cluster structure and core keywords in Fig 6 reflect some of the research hotspots in CRF in ovarian cancer patients. Mainly, cluster 0 is ovarian cancer and the core keyword is clinical trial. Cluster 1 is cancer-related distress, and the core keywords are chemotherapy and cancer survivor. Cluster 4 is wr2721 with core keywords ovarian cancer and paclitaxel. Cluster 5 is prognostic factors, and the core keyword is CRF. Cluster 11 is parp inhibitors, and the core keyword is bevacizumab. Cluster 12 is endometrial cancer, and the core keyword is quality of life. Cluster 13 is functional disability, and the core keyword was breast cancer.

Keyword bursts
"Burst words" are keywords that suddenly increase in citation frequency within a certain period, revealing the research hotspots and frontiers in the area [43][44][45]. The top 25 strongest burst words and the year of begin and end of each burst word in the field of CRF in ovarian cancer survivors as shown in (Fig 7). The green line in Fig 7 displays the time window of the documents retrieved in this study, and the red color indicates the duration of a certain burst word from its beginning to its end. The first burst keywords were cisplatin and colony stimulating factor in 1994, and the keywords with the highest burst strength were cyclophosphamide, double-blind, and open-label.

Discussion
Based on the WOSCC, this study applied CiteSpace to explore the research status of CRF in ovarian cancer patients, and analyze research hotspots and frontiers by analyzing 755 papers over 31 years.

The current research status for CRF in ovarian cancer survivors
The current state of research is described in terms of annual publications, research countries, institutions, researchers, journals, and cited journals [46]. As can be seen from the annual publications in this field, there has been no research in this area for the next two years since the first paper was published in 1991. Over the next decade, research in this area showed a slowgrowth trend, with consistently fewer than ten papers published per year. In the second Lancet Oncology, the highest impact factor among these 45 journals, which published a study of a randomized controlled trial showed that the proportion of patients with ovarian cancer accompanied with grade 3 fatigue in avelumab combination with pegylated liposomal doxorubicin (PLD) group and PLD alone group was 10 (5%) and 3 (2%), respectively [47]. It indicates that CRF is trigging increasing attention as an adverse reaction during cancer and anti-tumor treatments.

Research hotpots for CRF in ovarian cancer survivors
The co-occurrence network of high-frequency keywords represents a hot research topic in CRF in patients with ovarian cancer from 1991 to 2021. Since the search was conducted with ovarian cancer and CRF as the subject terms, although they appeared highest in the keyword co-occurrence knowledge map, it was not the focus of this paper; therefore, this was eliminated to obtain keywords with higher centrality and closer relevance to this study: chemotherapy, clinical trial, quality of life, cisplatin, paclitaxel, cancer survivor, and bevacizumab. As shown in Table 4, published articles on chemotherapy, clinical trial, and QoL were more than 100. In contrast, cancer survivor and the research on chemotherapeutic agents such as cisplatin, paclitaxel, and bevacizumab on fatigue are less studied and less published. The level of centrality is also a measure of the substantive impact that studying the hotspot can bring. The top 10 keywords with centrality greater than 0.1% were clinical trial, QoL, and cancer survivor.
Several surveys found that 43% [48]-53% [17] of patients with ovarian cancer complained of fatigue or extreme fatigue. CRF as a long-term side effect in cancer survivors negatively impacts the QoL of patients [18,49]. Home-based exercise, good relationships, and lifestyle interventions can improve QoL and reduce fatigue in patients with ovarian cancer [50,51]. Moreover, a randomized controlled trial study indicated that QoL was associated with overall survival in ovarian cancer patients [52]. In addition, studies have shown that chemotherapeutic agents such as cisplatin, paclitaxel, carboplatin, bevacizumab, and PARP inhibitors can also trigger fatigue in patients, and the percentage of fatigue caused ranges from 28%-35% [53][54][55]. Therefore, conducting multi-center, large-sample, randomized controlled clinical trials to determine whether chemotherapeutic agents have severe adverse effects in patients with ovarian cancer and to discuss the relationship between CRF and QoL and overall survival in cancer survivors are hotspots in this field.

Analysis of frontiers for CRF in ovarian cancer survivors
Analyzing keyword bursts could help researchers quickly understand the frontier or future trends in a particular field [56]. The highest strength burst keyword was cyclophosphamide with a strength of 9.85, which began in 1996 and ended in 2006. The research literature on cyclophosphamide in this period explored the effects of chemotherapeutic drug application on fatigue in patients with ovarian cancer. During the combination of different chemotherapeutic agents with cyclophosphamide, patients experienced fatigue grades ranging from grade 1 to 3, and the probability of occurrence varied from 8.3% [57] to 66% [58].
Double-blind ranked second with a strength of 8.88, which began in 2014 and persists till now. Double-blind is a vital principle in the design of randomized controlled trials, which can decrease bias due to subjective factors of subjects and researchers, although there are difficulties in its implementation [59]. We can learn from a randomized, double-blind study that ovarian cancer patients treated with two chemotherapeutic agents were more susceptible to fatigue than those treated with only one (87.7% vs. 74.1%) [60]. In another double-blind, placebo-controlled trial, the ratio of experiencing grade 1-2 fatigue and grade 3 fatigue in the trial group of patients with ovarian cancer was 62% and 4%, and the ratio of experiencing grade 1-2 fatigue and grade 3 fatigue in the placebo group was 37% and 2%, indicating that cancer itself is also a major contributor to fatigue [59].
Open-label ranked third with the strength of 8.59, which began in 2015 and continues until now. An open-label trial is a clinical trial in which both the investigators and participants know the drug or treatment being given [61]. Researchers have found that open-label placebo had a significant effect on some subjective symptoms [62]. In recent years, the application of open-label trials to determine the efficacy, safety, and tolerability of chemotherapeutic agents has become a new trend in scientific research [47,[63][64][65].

Strengths and limitations
To our knowledge, this is the first study to use the co-occurrence and co-citation analysis methods by CiteSpace to perform bibliometric analysis and visual display of CRF among ovarian cancer patients. Moreover, we deeply discuss the research status and explore the hotspots and frontiers in this field. There are also several limitations to this study. Due to the limitation of the bibliometric software, this study only retrieved data from the WOSCC, failing to include all research literature in the field of CRF in ovarian cancer survivors, which may be biased owing to insufficient research data. A comprehensive review of the literature to determine research hotspots and frontiers is an aspect that needs to be improved in future research.

Conclusions
In conclusion, research hotspots and frontiers in studies related to CRF in ovarian cancer patients in the past 31 years are always in dynamic change and difficult to precisely control. This review analyzes the research hotspots and frontiers in this area based on the existing literature to provide an evidence-based basis for policymakers and researchers. Conducting multicenter, large-sample, randomized controlled clinical trials to determine whether chemotherapeutic agents have severe adverse effects in ovarian cancer patients and to discuss the relationship between CRF and QoL and overall survival in cancer survivors are hotspots in this field. The new trends may be applying double-blind, randomized controlled trials to clarify the causes of cancer-related fatigue and open-label, randomized trials to determine the efficacy, safety, and tolerability of chemotherapeutic agents.